The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Using interpolation to improve path planning: The Field D* algorithm
Abstract
We present an interpolation‐based planning and replanning algorithm for generating low‐cost paths through uniform and nonuniform resolution grids. Most grid‐based path planners use discrete state transitions that artificially constrain an agent's motion to a small set of possible headings (e.g., 0, π/4, π/2, etc.). As a result, even “optimal” grid‐based planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and produce paths with a range of continuous headings. Consequently, it is particularly well suited to planning low‐cost trajectories for mobile robots. In this paper, we introduce a version of the algorithm for uniform resolution grids and a version for nonuniform resolution grids. Together, these approaches address two of the most significant shortcomings of grid‐based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids. We demonstrate our approaches on a number of example planning problems, compare them to related algorithms, and present several implementations on real robotic systems.
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Citations
Cites
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
LaValle, S. M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546877 · Cited by 5,211
LaValle, S. M. (2006) · Cited by 4,177
Principles of Artificial Intelligence
Nilsson, N. J. (1982) · doi:10.1007/978-3-662-09438-9 · Cited by 3,457 · cited 2×
“for replanning Hart et al., 1968; Nilsson, 1980; Stentz, 1995; Koenig & Likhachev, 2002b.”
BackgroundA fast marching level set method for monotonically advancing fronts.
Sethian, J. A. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.4.1591 · Cited by 3,221
The focussed D* algorithm for real-time replanning
Stentz, A. (1995) · Cited by 865
Self-citationPrinciples of Artificial Intelligence
(1981) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1981.4767059 · Cited by 847
ARA*: Anytime A* with Provable Bounds on Sub-Optimality
Likhachev, M. A., Gordon, G. J., & Thrun, S. (2003) · Cited by 643
Cited by
A Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
Path planning techniques for mobile robots: Review and prospect
Liu, L., Wang, X., Yang, X., Liu, H., Li, J., & Wang, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120254 · Cited by 707
Path Planning for the Mobile Robot: A Review
Zhang, H.-Y., Lin, W.-M., & Chen, A.-X. (2018) · Symmetry · doi:10.3390/sym10100450 · Cited by 447
Path Planning for Autonomous Underwater Vehicles
Petres, C., Pailhas, Y., Patron, P., Petillot, Y., Evans, J., & Lane, D. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.895057 · Cited by 433
Liu, P., Chen, A. Y., Huang, Y.-N., Han, J.-Y., Lai, J.-S., Kang, S.-C., Wu, T.-H., Wen, M.-C., & Tsai, M.-H. (2014) · Smart Structures and Systems · doi:10.12989/sss.2014.13.6.1065 · Cited by 377
Metadata
- Authors
- Ferguson, D., & Stentz, A.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2006-02
- Volume
- 23
- Issue
- 2
- Pages
- 79-101
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2003708116
- Cited by
- 393
- References
- 25
- FWCI
- 14.11
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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